Metrics for assessing community assembly processes
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Please cite the eBird Status and Trends data using:
Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, S. Ligocki, O. Robinson,
W. Hochachka, L. Jaromczyk, C. Crowley, K. Dunham, A. Stillman, I. Davies,
A. Rodewald, V. Ruiz-Gutierrez, C. Wood. 2023.
eBird Status and Trends, Data Version: 2022; Released: 2023. Cornell Lab of
Ornithology, Ithaca, New York. https://doi.org/10.2173/ebirdst.2022
This version of the package provides access to the 2022 version of the eBird
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doi:10.1111/2041-210X.12628
Shuangbin Xu, Lin Li, Xiao Luo, Meijun Chen, Wenli Tang, Li Zhan, Zehan Dai, Tommy T. Lam, Yi Guan, Guangchuang Yu. Ggtree:
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LG Wang, TTY Lam, S Xu, Z Dai, L Zhou, T Feng, P Guo, CW Dunn, BR Jones, T Bradley, H Zhu, Y Guan, Y Jiang, G Yu. treeio: an
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2020, 37(2):599-603. doi: 10.1093/molbev/msz240
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col_factorcommunity_data = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'community_assembly_metrics_using_relative_abundance.csv'))Rows: 341 Columns: 43── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (43): mntd_normalised, mntd_actual, mntd_min, mntd_max, mntd_mean, mntd_sd, fdiv_normalised, fdiv_actual, fdiv_min, fdiv_max, fdiv_...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(community_data)colnames(community_data) [1] "mntd_normalised" "mntd_actual" "mntd_min"
[4] "mntd_max" "mntd_mean" "mntd_sd"
[7] "fdiv_normalised" "fdiv_actual" "fdiv_min"
[10] "fdiv_max" "fdiv_mean" "fdiv_sd"
[13] "mass_fdiv_normalised" "mass_fdiv_actual" "mass_fdiv_min"
[16] "mass_fdiv_max" "mass_fdiv_mean" "mass_fdiv_sd"
[19] "locomotory_trait_fdiv_normalised" "locomotory_trait_fdiv_actual" "locomotory_trait_fdiv_min"
[22] "locomotory_trait_fdiv_max" "locomotory_trait_fdiv_mean" "locomotory_trait_fdiv_sd"
[25] "trophic_trait_fdiv_normalised" "trophic_trait_fdiv_actual" "trophic_trait_fdiv_min"
[28] "trophic_trait_fdiv_max" "trophic_trait_fdiv_mean" "trophic_trait_fdiv_sd"
[31] "gape_width_fdiv_normalised" "gape_width_fdiv_actual" "gape_width_fdiv_min"
[34] "gape_width_fdiv_max" "gape_width_fdiv_mean" "gape_width_fdiv_sd"
[37] "handwing_index_fdiv_normalised" "handwing_index_fdiv_actual" "handwing_index_fdiv_min"
[40] "handwing_index_fdiv_max" "handwing_index_fdiv_mean" "handwing_index_fdiv_sd"
[43] "city_id" Join on realms
city_to_realm = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv'))Rows: 342 Columns: 2── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): core_realm
dbl (1): city_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.community_data_with_realm = left_join(community_data, city_to_realm)Joining with `by = join_by(city_id)`Cities as points
city_points = st_centroid(read_sf(filename(CITY_DATA_OUTPUT_DIR, 'city_selection.shp'))) %>% left_join(community_data_with_realm)Warning: st_centroid assumes attributes are constant over geometriesWarning: st_centroid does not give correct centroids for longitude/latitude dataJoining with `by = join_by(city_id)`city_points_coords = st_coordinates(city_points)
city_points$latitude = city_points_coords[,1]
city_points$longitude = city_points_coords[,2]world_map = read_country_boundaries()Error: Cannot open "/Users/james/Dropbox/PhD/WorldBank_countries_Admin0_10m/WB_countries_Admin0_10m.shp"; The file doesn't seem to exist.Load community data, and create long format version
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))Rows: 2462 Columns: 12── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): city_name, jetz_species_name, seasonal, presence, origin
dbl (4): city_id, distance_to_northern_edge_km, distance_to_southern_edge_km, relative_abundance_proxy
lgl (3): present_urban_high, present_urban_med, present_urban_low
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.communitiescommunity_summary = communities %>% group_by(city_id) %>% summarise(regional_pool_size = n(), urban_pool_size = sum(relative_abundance_proxy > 0))
community_summaryLoad trait data
traits = read_csv(filename(TAXONOMY_OUTPUT_DIR, 'traits_jetz.csv'))Rows: 304 Columns: 6── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): jetz_species_name
dbl (5): gape_width, trophic_trait, locomotory_trait, mass, handwing_index
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(traits)Load realm geo
Summary metrics by Realm
test_required_values = function(name, df) {
cat(paste(
test_value_wilcox(paste(name, 'MNTD'), df$mntd_normalised),
test_value_wilcox(paste(name, 'Beak Gape FDiv'), df$gape_width_fdiv_normalised),
test_value_wilcox(paste(name, 'HWI FDiv'), df$handwing_index_fdiv_normalised),
test_value_wilcox(paste(name, 'Mass FDiv'), df$mass_fdiv_normalised),
nrow(df),
sep = "\n"))
}test_required_values('Global', community_data_with_realm)Global MNTD median 0.49
Global Beak Gape FDiv median 0.59 ***
Global HWI FDiv median 0.66 ***
Global Mass FDiv median 0.65 ***
341unique(community_data_with_realm$core_realm)[1] "Oceania" "Nearctic" "Neotropic" "Palearctic" "Afrotropic" "Indomalayan" "Australasia"test_required_values('Nearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Nearctic',])Warning: cannot compute exact p-value with tiesWarning: cannot compute exact p-value with tiesNearctic MNTD median 0.65
Nearctic Beak Gape FDiv median 0.63
Nearctic HWI FDiv median 0.23 **
Nearctic Mass FDiv median 0.48
66test_required_values('Neotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Neotropic',])Neotropic MNTD median 0.53
Neotropic Beak Gape FDiv median 0.46
Neotropic HWI FDiv median 0.51
Neotropic Mass FDiv median 0.65 ***
64test_required_values('Palearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Palearctic',])Palearctic MNTD median 0.59 *
Palearctic Beak Gape FDiv median 0.93 ***
Palearctic HWI FDiv median 0.4
Palearctic Mass FDiv median 0.55
74test_required_values('Afrotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Afrotropic',])Afrotropic MNTD median 0.17 *
Afrotropic Beak Gape FDiv median 0.4
Afrotropic HWI FDiv median 0.54
Afrotropic Mass FDiv median 0.34
9test_required_values('Indomalayan', community_data_with_realm[community_data_with_realm$core_realm == 'Indomalayan',])Indomalayan MNTD median 0.43 ***
Indomalayan Beak Gape FDiv median 0.47
Indomalayan HWI FDiv median 0.88 ***
Indomalayan Mass FDiv median 0.81 ***
121test_required_values('Australasia', community_data_with_realm[community_data_with_realm$core_realm == 'Australasia',])Australasia MNTD median 0.53
Australasia Beak Gape FDiv median 0.46
Australasia HWI FDiv median 0.79
Australasia Mass FDiv median 0.55
6Summary metrics by introduced species
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))Rows: 2462 Columns: 12── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): city_name, jetz_species_name, seasonal, presence, origin
dbl (4): city_id, distance_to_northern_edge_km, distance_to_southern_edge_km, relative_abundance_proxy
lgl (3): present_urban_high, present_urban_med, present_urban_low
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.city_introduced_species = communities %>% group_by(city_id) %>% summarise(number_of_species = n()) %>% left_join(
communities %>% group_by(city_id) %>% filter(origin == 'Introduced') %>% summarise(number_of_introduced_species = n())
) %>% replace_na(list(number_of_introduced_species = 0))Joining with `by = join_by(city_id)`community_data_with_introductions = left_join(community_data, city_introduced_species)Joining with `by = join_by(city_id)`community_data_with_introductions$has_introduced_species = community_data_with_introductions$number_of_introduced_species > 0
community_data_with_introductionscommunity_data_with_introductions[,c('mntd_normalised', 'has_introduced_species')]community_data_with_introductions %>% group_by(has_introduced_species) %>% summarise(
total_cities = n(),
mean_mntd_normalised = mean(mntd_normalised, na.rm = T),
sd_mntd_normalised = sd(mntd_normalised, na.rm = T),
mean_mass_fdiv_normalised = mean(mass_fdiv_normalised, na.rm = T),
sd_mass_fdiv_normalised = sd(mass_fdiv_normalised, na.rm = T),
mean_gape_width_fdiv_normalised = mean(gape_width_fdiv_normalised, na.rm = T),
sd_gape_width_fdiv_normalised = sd(gape_width_fdiv_normalised, na.rm = T),
mean_handwing_index_fdiv_normalised = mean(handwing_index_fdiv_normalised, na.rm = T),
sd_handwing_index_fdiv_normalised = sd(handwing_index_fdiv_normalised, na.rm = T)
)MNTD
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_normalised)) + geom_boxplot()
wilcox.test(mntd_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: mntd_normalised by has_introduced_species
W = 10956, p-value = 0.02272
alternative hypothesis: true location shift is not equal to 0There is a significant difference between the response of cities with introduced species (0.53±0.27) and those without (0.47±0.19) (p-value = 0.02).
Mass FDiv
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_normalised)) + geom_boxplot()
wilcox.test(mass_fdiv_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: mass_fdiv_normalised by has_introduced_species
W = 17286, p-value = 0.00000004887
alternative hypothesis: true location shift is not equal to 0There is a significant difference between the response of cities with introduced species (0.57±0.27) and those without (0.73±0.24) (p < 0.0001)
Beak Gape FDiv
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = gape_width_fdiv_normalised)) + geom_boxplot()
wilcox.test(gape_width_fdiv_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: gape_width_fdiv_normalised by has_introduced_species
W = 10990, p-value = 0.1178
alternative hypothesis: true location shift is not equal to 0There is NOT a significant difference between the response of cities with introduced species (0.61±0.30) and those without (0.56±0.27)
HWI FDiv
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = handwing_index_fdiv_normalised)) + geom_boxplot()
wilcox.test(handwing_index_fdiv_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
Wilcoxon rank sum test with continuity correction
data: handwing_index_fdiv_normalised by has_introduced_species
W = 19410, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0There is a significant difference between the response of cities with introduced species (0.49±0.30) and those without (0.79±0.21) (p < 0.0001)
Examine individual metrics
Analysis data frame
geography = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'geography.csv'))Rows: 342 Columns: 26── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (26): city_id, city_avg_ndvi, city_avg_elevation, city_avg_temp, city_avg_min_monthly_temp, city_avg_max_monthly_temp, city_avg_mon...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.names(geography) [1] "city_id" "city_avg_ndvi" "city_avg_elevation" "city_avg_temp"
[5] "city_avg_min_monthly_temp" "city_avg_max_monthly_temp" "city_avg_monthly_temp" "city_avg_rainfall"
[9] "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall" "city_avg_soil_moisture" "city_max_elev"
[13] "city_min_elev" "city_elev_range" "region_20km_avg_ndvi" "region_20km_avg_elevation"
[17] "region_20km_avg_soil_moisture" "region_20km_max_elev" "region_20km_min_elev" "region_20km_elev_range"
[21] "region_50km_avg_ndvi" "region_50km_avg_elevation" "region_50km_avg_soil_moisture" "region_50km_max_elev"
[25] "region_50km_min_elev" "region_50km_elev_range" analysis_data = community_data_with_realm[,c('city_id', 'mntd_normalised', 'mass_fdiv_normalised', 'gape_width_fdiv_normalised', 'handwing_index_fdiv_normalised', 'core_realm')] %>%
left_join(city_points[,c('city_id', 'latitude', 'longitude')]) %>%
left_join(community_data_with_introductions[,c('city_id', 'has_introduced_species')]) %>%
left_join(geography)Joining with `by = join_by(city_id)`Joining with `by = join_by(city_id)`Joining with `by = join_by(city_id)`analysis_data$abs_latitude = abs(analysis_data$latitude)
analysis_data$core_realm = factor(analysis_data$core_realm, levels = c('Palearctic', 'Nearctic', 'Neotropic', 'Afrotropic', 'Indomalayan', 'Australasia', 'Oceania'))
analysis_data$has_introduced_species = factor(analysis_data$has_introduced_species, level = c('TRUE', 'FALSE'), labels = c('Introduced species', 'No introduced species'))model_data = function(df, dependant_var) {
df[,c(dependant_var, 'core_realm', 'abs_latitude', 'latitude', 'longitude', 'has_introduced_species', 'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range', 'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 'region_20km_min_elev', 'region_20km_elev_range', 'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 'region_50km_min_elev', 'region_50km_elev_range')]
}
model_data(analysis_data, 'mntd_normalised')names(analysis_data) [1] "city_id" "mntd_normalised" "mass_fdiv_normalised" "gape_width_fdiv_normalised"
[5] "handwing_index_fdiv_normalised" "core_realm" "latitude" "longitude"
[9] "geometry" "has_introduced_species" "city_avg_ndvi" "city_avg_elevation"
[13] "city_avg_temp" "city_avg_min_monthly_temp" "city_avg_max_monthly_temp" "city_avg_monthly_temp"
[17] "city_avg_rainfall" "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall" "city_avg_soil_moisture"
[21] "city_max_elev" "city_min_elev" "city_elev_range" "region_20km_avg_ndvi"
[25] "region_20km_avg_elevation" "region_20km_avg_soil_moisture" "region_20km_max_elev" "region_20km_min_elev"
[29] "region_20km_elev_range" "region_50km_avg_ndvi" "region_50km_avg_elevation" "region_50km_avg_soil_moisture"
[33] "region_50km_max_elev" "region_50km_min_elev" "region_50km_elev_range" "abs_latitude" all_explanatories = c(
'abs_latitude', 'latitude', 'longitude',
'has_introduced_species',
'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp',
'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall',
'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range',
'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev',
'region_20km_min_elev', 'region_20km_elev_range',
'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev',
'region_50km_min_elev', 'region_50km_elev_range',
'core_realmAfrotropic', 'core_realmAustralasia', 'core_realmIndomalayan', 'core_realmNearctic', 'core_realmNeotropic', 'core_realmPalearctic')
type_labels = function(p) {
explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
p$type <- 'Realm'
p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp',
'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall',
'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range')] <- 'City geography'
p$type[p$explanatory %in% c('region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev',
'region_50km_min_elev', 'region_50km_elev_range')] <- 'Regional (50km) geography'
p$type[p$explanatory %in% c('region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev',
'region_20km_min_elev', 'region_20km_elev_range')] <- 'Regional (20km) geography'
p$type[p$explanatory %in% c('abs_latitude', 'latitude', 'longitude')] <- 'Spatial'
p
}explanatory_labels = c(
'has_introduced_species'='Has introduced species',
'city_avg_ndvi'='Average NDVI',
'city_avg_elevation'='Average elevation',
'city_avg_temp'='Average temperature',
'city_avg_min_monthly_temp'='Average minimum monthly temperature',
'city_avg_max_monthly_temp'='Average maximum monthly temperature',
'city_avg_monthly_temp'='Average monthly temperature',
'city_avg_rainfall'='Average rainfall',
'city_avg_max_monthly_rainfall'='Average maximum monthly rainfall',
'city_avg_min_monthly_rainfall'='Average minimum monthly rainfall',
'city_avg_soil_moisture'='Average soil moisture',
'city_max_elev'='Maximum elevation',
'city_min_elev'='Minimum elevation',
'city_elev_range'='Elevation range',
'region_20km_avg_ndvi'='Average NDVI',
'region_20km_avg_elevation'='Average elevation',
'region_20km_avg_soil_moisture'='Average soil moisture',
'region_20km_max_elev'='Maximum elevation',
'region_20km_min_elev'='Minimum elevation',
'region_20km_elev_range'='Elevation range',
'region_50km_avg_ndvi'='Average NDVI',
'region_50km_avg_elevation'='Average elevation',
'region_50km_avg_soil_moisture'='Average soil moisture',
'region_50km_max_elev'='Maximum elevation',
'region_50km_min_elev'='Minimum elevation',
'region_50km_elev_range'='Elevation range',
'abs_latitude' = 'Absolute latitude',
'latitude' = 'Latitude',
'longitude' = 'Longitude',
'core_realmAfrotropic' = 'Afrotropical',
'core_realmAustralasia' = 'Austaliasian',
'core_realmIndomalayan' = 'Indomalayan',
'core_realmNearctic' = 'Nearctic',
'core_realmNeotropic' = 'Neotropical',
'core_realmPalearctic' = 'Palearctic',
'core_realmOceania' = 'Oceanical')Helper plot functions
geom_normalised_histogram = function(name, gg, legend.position = "bottom") {
gg +
geom_histogram(aes(fill = core_realm), binwidth = 0.1, position = "dodge") +
geom_vline(aes(xintercept = 0.5), color = "#000000", size = 0.4) +
geom_vline(aes(xintercept = 0), color = "#000000", size = 0.2, linetype = "dashed") +
geom_vline(aes(xintercept = 1), color = "#000000", size = 0.2, linetype = "dashed") +
ylab("Number of cities") + xlab("Normalised Response") + ylim(c(0, 70)) +
labs(title = name, fill = 'Realm') +
theme_bw() +
theme(legend.position=legend.position)
}geom_map = function(map_sf, title) {
norm_mntd_analysis_geo = ggplot() +
geom_sf(data = world_map, aes(geometry = geometry)) +
map_sf +
normalised_colours_scale +
labs(colour = 'Normalised\nResponse') +
theme_bw() +
theme(legend.position="bottom")
}Helper Dredge functions
# Taken from MuMIN package
# https://rdrr.io/cran/MuMIn/src/R/averaging.R
# https://rdrr.io/cran/MuMIn/src/R/model.avg.R
.coefarr.avg <-
function(cfarr, weight, revised.var, full, alpha) {
weight <- weight / sum(weight)
nCoef <- dim(cfarr)[3L]
if(full) {
nas <- is.na(cfarr[, 1L, ]) & is.na(cfarr[, 2L, ])
cfarr[, 1L, ][nas] <- cfarr[, 2L, ][nas] <- 0
#cfarr[, 1L:2L, ][is.na(cfarr[, 1L:2L, ])] <- 0
if(!all(is.na(cfarr[, 3L, ])))
cfarr[ ,3L, ][is.na(cfarr[ , 3L, ])] <- Inf
}
avgcoef <- array(dim = c(nCoef, 5L),
dimnames = list(dimnames(cfarr)[[3L]], c("Estimate",
"Std. Error", "Adjusted SE", "Lower CI", "Upper CI")))
for(i in seq_len(nCoef))
avgcoef[i, ] <- par.avg(cfarr[, 1L, i], cfarr[, 2L, i], weight,
df = cfarr[, 3L, i], alpha = alpha, revised.var = revised.var)
avgcoef[is.nan(avgcoef)] <- NA
return(avgcoef)
}
.makecoefmat <- function(cf) {
no.ase <- all(is.na(cf[, 3L]))
z <- abs(cf[, 1L] / cf[, if(no.ase) 2L else 3L])
pval <- 2 * pnorm(z, lower.tail = FALSE)
cbind(cf[, if(no.ase) 1L:2L else 1L:3L, drop = FALSE],
`z value` = z, `Pr(>|z|)` = zapsmall(pval))
}
# Generate model selections using lmer, dredge, and model.avg
# `forumla` : a two-sided linear formula object describing both the fixed-effects and random-effects part of the model
# `data` : the data frame containing the variables from the formula
# `aic_delta` : the AIC delta to use for selecting models in model average
model_average <- function(formula, data, aic_delta = 20) {
model <- lm(
formula,
data=data
)
dredge_result <- dredge(model)
summary(model.avg(dredge_result, subset = delta < aic_delta))
}
# Create a summary data frame containing the selected variables from a model
# `model_sum` : The model summary output from `model_average`
model_summary <- function(model_sum) {
.column_name <- function(postfix) {
postfix
}
# just return the estimate and p value
weight <- model_sum$msTable[, 5L]
coefmat.full <- as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
attr(model_sum, "revised.var"), TRUE, 0.05)))
coefmat.subset <-
as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
attr(model_sum, "revised.var"), FALSE, 0.05)))
coefmat.subset <- coefmat.subset[-c(1), c(1, 2, 5)]
names(coefmat.subset) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
coefmat.subset <- tibble::rownames_to_column(coefmat.subset, "explanatory")
coefmat.subset$model = 'subset'
coefmat.full <- coefmat.full[-c(1), c(1, 2, 5)]
names(coefmat.full) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
coefmat.full <- tibble::rownames_to_column(coefmat.full, "explanatory")
coefmat.full$model = 'full'
rbind(coefmat.full, coefmat.subset)
}formula_from_vsurp = function(predictors, dependent, vsurp_result) {
as.formula(paste(dependent, paste(names(predictors[,vsurp_result$varselect.interp]), collapse="+"), sep = "~"))
}plot_vsurp_result = function(result_table) {
plot = result_table[result_table$model == 'full',]
plot = type_labels(plot)
ggplot(plot, aes(y=explanatory, x=estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=estimate-error, xmax=estimate+error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(plot$explanatory)),
labels = explanatory_labels) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase from 0 (habitat filtering)\nto 1 (competitive exclusion)\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
}MNTD
norm_mntd_analysis_plot = geom_normalised_histogram(
'MNTD',
ggplot(analysis_data, aes(mntd_normalised))
)
norm_mntd_analysis_plot
norm_mntd_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = mntd_normalised, geometry = geometry)), 'MNTD')
norm_mntd_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_mntd_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
norm_mntd_analysis_data = model_data(analysis_data[!is.na(analysis_data$mntd_normalised),], 'mntd_normalised')
norm_mntd_analysis_predictors = norm_mntd_analysis_data[,-1]
norm_mntd_analysis_interp = VSURF(x = norm_mntd_analysis_predictors, y = norm_mntd_analysis_data$mntd_normalised)Thresholding step
Estimated computational time (on one core): 18 sec.
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Interpretation step (on 29 variables)
Estimated computational time (on one core): between 7 sec. and 31.3 sec.
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Prediction step (on 8 variables)
Maximum estimated computational time (on one core): 2.6 sec.
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|=================================================================================================================================| 100%names(norm_mntd_analysis_predictors[,norm_mntd_analysis_interp$varselect.interp])[1] "city_avg_monthly_temp" "city_avg_max_monthly_temp" "core_realm" "longitude"
[5] "city_avg_temp" "latitude" "abs_latitude" "city_avg_min_monthly_temp"norm_mntd_analysis_formula = formula_from_vsurp(norm_mntd_analysis_predictors, "mntd_normalised", norm_mntd_analysis_interp)
norm_mntd_analysis_result <- model_average(norm_mntd_analysis_formula, norm_mntd_analysis_data)Fixed term is "(Intercept)"norm_mntd_analysis_result_table = model_summary(norm_mntd_analysis_result)
norm_mntd_analysis_result_tablenorm_mntd_analysis_pred_plot = plot_vsurp_result(norm_mntd_analysis_result_table)
norm_mntd_analysis_pred_plot
Gape width - FDiv
norm_gape_fdiv_analysis_plot = geom_normalised_histogram(
'Gape FDiv',
ggplot(analysis_data, aes(gape_width_fdiv_normalised))
)
norm_gape_fdiv_analysis_plot
norm_gape_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = gape_width_fdiv_normalised, geometry = geometry)), 'Gape Width FDiv')
norm_gape_fdiv_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_gape_width_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
norm_gape_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$gape_width_fdiv_normalised),], 'gape_width_fdiv_normalised')
norm_gape_fdiv_analysis_predictors = norm_gape_fdiv_analysis_data[,-1]
norm_gape_fdiv_analysis_interp = VSURF(x = norm_gape_fdiv_analysis_predictors, y = norm_gape_fdiv_analysis_data$gape_width_fdiv_normalised)Thresholding step
Estimated computational time (on one core): 16.1 sec.
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Interpretation step (on 30 variables)
Estimated computational time (on one core): between 6.3 sec. and 33.3 sec.
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Prediction step (on 11 variables)
Maximum estimated computational time (on one core): 4.5 sec.
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|=================================================================================================================================| 100%names(norm_gape_fdiv_analysis_predictors[,norm_gape_fdiv_analysis_interp$varselect.interp]) [1] "longitude" "core_realm" "abs_latitude" "city_avg_max_monthly_temp"
[5] "city_avg_temp" "city_avg_min_monthly_temp" "city_avg_max_monthly_rainfall" "city_avg_min_monthly_rainfall"
[9] "latitude" "city_avg_monthly_temp" "city_max_elev" norm_gape_fdiv_analysis_formula = formula_from_vsurp(norm_gape_fdiv_analysis_predictors, "gape_width_fdiv_normalised", norm_gape_fdiv_analysis_interp)
norm_gape_fdiv_analysis_result <- model_average(norm_gape_fdiv_analysis_formula, norm_gape_fdiv_analysis_data)Fixed term is "(Intercept)"norm_gape_fdiv_analysis_result_table = model_summary(norm_gape_fdiv_analysis_result)
norm_gape_fdiv_analysis_result_tablenorm_gape_fdiv_analysis_pred_plot = plot_vsurp_result(norm_gape_fdiv_analysis_result_table)
norm_gape_fdiv_analysis_pred_plot
HWI - FDiv
norm_hwi_fdiv_loco_analysis_plot = geom_normalised_histogram(
'HWI FDiv',
ggplot(analysis_data, aes(handwing_index_fdiv_normalised))
)
norm_hwi_fdiv_loco_analysis_plot
norm_hwi_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = handwing_index_fdiv_normalised, geometry = geometry)), 'HWI FDiv')
norm_hwi_fdiv_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_hwi_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
norm_hwi_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$handwing_index_fdiv_normalised),], 'handwing_index_fdiv_normalised')
norm_hwi_fdiv_analysis_predictors = norm_hwi_fdiv_analysis_data[,-1]
norm_hwi_fdiv_analysis_interp = VSURF(x = norm_hwi_fdiv_analysis_predictors, y = norm_hwi_fdiv_analysis_data$handwing_index_fdiv_normalised)Thresholding step
Estimated computational time (on one core): 14.2 sec.
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Interpretation step (on 30 variables)
Estimated computational time (on one core): between 6 sec. and 29.1 sec.
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Prediction step (on 8 variables)
Maximum estimated computational time (on one core): 2.3 sec.
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|=================================================================================================================================| 100%names(norm_hwi_fdiv_analysis_predictors[,norm_hwi_fdiv_analysis_interp$varselect.interp])[1] "latitude" "core_realm" "city_avg_max_monthly_temp" "city_avg_temp"
[5] "longitude" "city_avg_max_monthly_rainfall" "region_50km_elev_range" "region_50km_min_elev" norm_hwi_fdiv_analysis_formula = formula_from_vsurp(norm_hwi_fdiv_analysis_predictors, "handwing_index_fdiv_normalised", norm_hwi_fdiv_analysis_interp)
norm_hwi_fdiv_analysis_result <- model_average(norm_hwi_fdiv_analysis_formula, norm_hwi_fdiv_analysis_data)Fixed term is "(Intercept)"norm_hwi_fdiv_analysis_result_table = model_summary(norm_hwi_fdiv_analysis_result)
norm_hwi_fdiv_analysis_result_tablenorm_hwi_fdiv_analysis_pred_plot = plot_vsurp_result(norm_hwi_fdiv_analysis_result_table)
norm_hwi_fdiv_analysis_pred_plot
Mass - FDiv
norm_mass_fdiv_loco_analysis_plot = geom_normalised_histogram(
'Mass FDiv',
ggplot(analysis_data, aes(mass_fdiv_normalised))
)
norm_mass_fdiv_loco_analysis_plot
norm_mass_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = mass_fdiv_normalised, geometry = geometry)), 'Mass FDiv')
norm_mass_fdiv_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_mass_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
norm_mass_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$mass_fdiv_normalised),], 'mass_fdiv_normalised')
norm_mass_fdiv_analysis_predictors = norm_mass_fdiv_analysis_data[,-1]
norm_mass_fdiv_analysis_interp = VSURF(x = norm_mass_fdiv_analysis_predictors, y = norm_mass_fdiv_analysis_data$mass_fdiv_normalised)Thresholding step
Estimated computational time (on one core): 15.8 sec.
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Interpretation step (on 30 variables)
Estimated computational time (on one core): between 1.5 sec. and 32.4 sec.
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Prediction step (on 5 variables)
Maximum estimated computational time (on one core): 0.9 sec.
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|=================================================================================================================================| 100%names(norm_mass_fdiv_analysis_predictors[,norm_mass_fdiv_analysis_interp$varselect.interp])[1] "core_realm" "abs_latitude" "city_avg_temp" "latitude"
[5] "city_avg_max_monthly_rainfall"norm_mass_fdiv_analysis_formula = formula_from_vsurp(norm_mass_fdiv_analysis_predictors, "mass_fdiv_normalised", norm_mass_fdiv_analysis_interp)
norm_mass_fdiv_analysis_result <- model_average(norm_mass_fdiv_analysis_formula, norm_mass_fdiv_analysis_data)Fixed term is "(Intercept)"norm_mass_fdiv_analysis_result_table = model_summary(norm_mass_fdiv_analysis_result)
norm_mass_fdiv_analysis_result_tablenorm_mass_fdiv_analysis_pred_plot = plot_vsurp_result(norm_mass_fdiv_analysis_result_table)
norm_mass_fdiv_analysis_pred_plot
Create plot of differences in process response
pred_legend <- get_legend(
# create some space to the left of the legend
norm_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, 12))
)Warning: Removed 1 row containing missing values (`geom_line()`).`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?Warning: Removed 1 rows containing missing values (`geom_point()`).geo_legend <- get_legend(
# create some space to the left of the legend
norm_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(0, 0, 0, 12))
)
plot_grid(
plot_grid(
norm_mntd_analysis_geo_plot + theme(legend.position="none"),
norm_mntd_analysis_pred_plot + theme(legend.position="none"),
nrow = 1
) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
norm_gape_fdiv_analysis_geo_plot + theme(legend.position="none"),
norm_gape_fdiv_analysis_pred_plot + theme(legend.position="none"),
nrow = 1
) + draw_label("Gape", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
norm_hwi_fdiv_analysis_geo_plot + theme(legend.position="none"),
norm_hwi_fdiv_analysis_pred_plot + theme(legend.position="none"),
nrow = 1
) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
norm_mass_fdiv_analysis_geo_plot + theme(legend.position="none"),
norm_mass_fdiv_analysis_pred_plot + theme(legend.position="none"),
nrow = 1
) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5),
plot_grid(
geo_legend,
pred_legend,
nrow = 1
),
nrow = 5
)`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?Warning: Removed 1 row containing missing values (`geom_line()`).`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?Warning: Removed 1 rows containing missing values (`geom_point()`).Warning: Removed 1 row containing missing values (`geom_line()`).`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?Warning: Removed 1 rows containing missing values (`geom_point()`).
ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response.jpg'), width = 2500, height=5000, units = 'px')
Compare metrics against each other
ggplot(analysis_data, aes(x = gape_width_fdiv_normalised, y = mntd_normalised, colour = core_realm)) +
geom_point() +
ylab("MNTD") +
xlab("Gape Width FDiv") +
theme_bw() + labs(color = "Realm")
ggplot(analysis_data, aes(x = handwing_index_fdiv_normalised, y = mntd_normalised, colour = core_realm)) +
geom_point() +
ylab("MNTD") +
xlab("HWI FDiv") +
theme_bw() + labs(color = "Realm")
ggplot(analysis_data, aes(x = handwing_index_fdiv_normalised, y = gape_width_fdiv_normalised, colour = core_realm)) +
geom_point() +
ylab("Gape Width FDiv") +
xlab("HWI FDiv") +
theme_bw() + labs(color = "Realm")
mntd_fdiv_analysis = analysis_data %>%
dplyr::select(city_id, mntd_normalised, handwing_index_fdiv_normalised, gape_width_fdiv_normalised) %>%
left_join(community_summary) %>%
mutate(urban_pool_perc = urban_pool_size * 100 / regional_pool_size)Joining with `by = join_by(city_id)`mntd_fdiv_analysisggpairs(mntd_fdiv_analysis %>% dplyr::select(mntd_normalised, handwing_index_fdiv_normalised, gape_width_fdiv_normalised, regional_pool_size, urban_pool_size, urban_pool_perc))
ggsave(filename(FIGURES_OUTPUT_DIR, 'appendix_normalised_correlation.jpg'))Saving 7.29 x 4.51 in image
---
title: "Metrics for assessing community assembly processes"
output: html_notebook
bibliography: ../ref.bib  
---

```{r}
source('../env.R')
```

```{r}
community_data = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'community_assembly_metrics_using_relative_abundance.csv'))
head(community_data)
colnames(community_data)
```

Join on realms
```{r}
city_to_realm = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'realms.csv'))
community_data_with_realm = left_join(community_data, city_to_realm)
```

Cities as points
```{r}
city_points = st_centroid(read_sf(filename(CITY_DATA_OUTPUT_DIR, 'city_selection.shp'))) %>% left_join(community_data_with_realm)
city_points_coords = st_coordinates(city_points)
city_points$latitude = city_points_coords[,1]
city_points$longitude = city_points_coords[,2]
```
  
```{r}
world_map = read_country_boundaries()
```

Load community data, and create long format version
```{r}
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))
communities
```

```{r}
community_summary = communities %>% group_by(city_id) %>% summarise(regional_pool_size = n(), urban_pool_size = sum(relative_abundance_proxy > 0))
community_summary
```

Load trait data
```{r}
traits = read_csv(filename(TAXONOMY_OUTPUT_DIR, 'traits_jetz.csv'))
head(traits)
```

Load realm geo
```{r}
resolve = read_resolve()
head(resolve)
```

# Summary metrics by Realm
```{r}
test_required_values = function(name, df) {
  cat(paste(
    test_value_wilcox(paste(name, 'MNTD'), df$mntd_normalised),
    test_value_wilcox(paste(name, 'Beak Gape FDiv'), df$gape_width_fdiv_normalised),
    test_value_wilcox(paste(name, 'HWI FDiv'), df$handwing_index_fdiv_normalised),
    test_value_wilcox(paste(name, 'Mass FDiv'), df$mass_fdiv_normalised),
    nrow(df),
    sep = "\n"))
}
```

```{r}
test_required_values('Global', community_data_with_realm)
```

```{r}
unique(community_data_with_realm$core_realm)
```

```{r}
test_required_values('Nearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Nearctic',])
```

```{r}
test_required_values('Neotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Neotropic',])
```

```{r}
test_required_values('Palearctic', community_data_with_realm[community_data_with_realm$core_realm == 'Palearctic',])
```

```{r}
test_required_values('Afrotropic', community_data_with_realm[community_data_with_realm$core_realm == 'Afrotropic',])
```

```{r}
test_required_values('Indomalayan', community_data_with_realm[community_data_with_realm$core_realm == 'Indomalayan',])
```

```{r}
test_required_values('Australasia', community_data_with_realm[community_data_with_realm$core_realm == 'Australasia',])
```

# Summary metrics by introduced species
```{r}
communities = read_csv(filename(COMMUNITY_OUTPUT_DIR, 'communities_for_analysis.csv'))
city_introduced_species = communities %>% group_by(city_id) %>% summarise(number_of_species = n()) %>% left_join(
  communities %>% group_by(city_id) %>% filter(origin == 'Introduced') %>% summarise(number_of_introduced_species = n())
) %>% replace_na(list(number_of_introduced_species = 0))

community_data_with_introductions = left_join(community_data, city_introduced_species)
community_data_with_introductions$has_introduced_species = community_data_with_introductions$number_of_introduced_species > 0
community_data_with_introductions
```

```{r}
community_data_with_introductions[,c('mntd_normalised', 'has_introduced_species')]
```

```{r}
community_data_with_introductions %>% group_by(has_introduced_species) %>% summarise(
  total_cities = n(), 
  
  mean_mntd_normalised = mean(mntd_normalised, na.rm = T),
  sd_mntd_normalised = sd(mntd_normalised, na.rm = T),
  
  mean_mass_fdiv_normalised = mean(mass_fdiv_normalised, na.rm = T),
  sd_mass_fdiv_normalised = sd(mass_fdiv_normalised, na.rm = T),
  
  mean_gape_width_fdiv_normalised = mean(gape_width_fdiv_normalised, na.rm = T),
  sd_gape_width_fdiv_normalised = sd(gape_width_fdiv_normalised, na.rm = T),
  
  mean_handwing_index_fdiv_normalised = mean(handwing_index_fdiv_normalised, na.rm = T),
  sd_handwing_index_fdiv_normalised = sd(handwing_index_fdiv_normalised, na.rm = T)
)
```

## MNTD
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mntd_normalised)) + geom_boxplot()
```

```{r}
wilcox.test(mntd_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```

There is a significant difference between the response of cities with introduced species (0.53±0.27) and those without (0.47±0.19) (p-value = 0.02).


## Mass FDiv
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = mass_fdiv_normalised)) + geom_boxplot()
```

```{r}
wilcox.test(mass_fdiv_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```
There is a significant difference between the response of cities with introduced species (0.57±0.27) and those without (0.73±0.24) (p < 0.0001)


## Beak Gape FDiv
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = gape_width_fdiv_normalised)) + geom_boxplot()
```

```{r}
wilcox.test(gape_width_fdiv_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```
There is NOT a significant difference between the response of cities with introduced species (0.61±0.30) and those without (0.56±0.27)


## HWI FDiv
```{r}
ggplot(community_data_with_introductions, aes(x = has_introduced_species, y = handwing_index_fdiv_normalised)) + geom_boxplot()
```

```{r}
wilcox.test(handwing_index_fdiv_normalised ~ has_introduced_species, community_data_with_introductions, na.action = 'na.omit')
```
There is a significant difference between the response of cities with introduced species (0.49±0.30) and those without (0.79±0.21) (p < 0.0001)


# Examine individual metrics

## Analysis data frame
```{r}
geography = read_csv(filename(CITY_DATA_OUTPUT_DIR, 'geography.csv'))
names(geography)
```

```{r}
analysis_data = community_data_with_realm[,c('city_id', 'mntd_normalised', 'mass_fdiv_normalised', 'gape_width_fdiv_normalised', 'handwing_index_fdiv_normalised', 'core_realm')] %>% 
  left_join(city_points[,c('city_id', 'latitude', 'longitude')]) %>%
  left_join(community_data_with_introductions[,c('city_id', 'has_introduced_species')]) %>%
  left_join(geography)

analysis_data$abs_latitude = abs(analysis_data$latitude)
analysis_data$core_realm = factor(analysis_data$core_realm, levels = c('Palearctic', 'Nearctic', 'Neotropic', 'Afrotropic', 'Indomalayan', 'Australasia', 'Oceania'))
analysis_data$has_introduced_species = factor(analysis_data$has_introduced_species, level = c('TRUE', 'FALSE'), labels = c('Introduced species', 'No introduced species'))
```

```{r}
model_data = function(df, dependant_var) {
  df[,c(dependant_var, 'core_realm', 'abs_latitude', 'latitude', 'longitude', 'has_introduced_species', 'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range', 'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 'region_20km_min_elev', 'region_20km_elev_range', 'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 'region_50km_min_elev', 'region_50km_elev_range')]
}
model_data(analysis_data, 'mntd_normalised')
```

```{r}
names(analysis_data)
```

```{r}
all_explanatories = c(
    'abs_latitude', 'latitude', 'longitude', 
    'has_introduced_species',
    'city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 
    'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 
    'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range',
    'region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 
    'region_20km_min_elev', 'region_20km_elev_range',
    'region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 
    'region_50km_min_elev', 'region_50km_elev_range',
    'core_realmAfrotropic', 'core_realmAustralasia', 'core_realmIndomalayan', 'core_realmNearctic', 'core_realmNeotropic', 'core_realmPalearctic')

type_labels = function(p) {
  explanatory_levels = all_explanatories[all_explanatories %in% p$explanatory]
  p$explanatory <- factor(p$explanatory, levels = explanatory_levels)
  
  p$type <- 'Realm'
  p$type[p$explanatory %in% c('city_avg_ndvi', 'city_avg_elevation', 'city_avg_temp', 'city_avg_min_monthly_temp', 'city_avg_max_monthly_temp', 
    'city_avg_monthly_temp', 'city_avg_rainfall', 'city_avg_max_monthly_rainfall', 'city_avg_min_monthly_rainfall', 
    'city_avg_soil_moisture', 'city_max_elev', 'city_min_elev', 'city_elev_range')] <- 'City geography'
  p$type[p$explanatory %in% c('region_50km_avg_ndvi', 'region_50km_avg_elevation', 'region_50km_avg_soil_moisture', 'region_50km_max_elev', 
    'region_50km_min_elev', 'region_50km_elev_range')] <- 'Regional (50km) geography'
   p$type[p$explanatory %in% c('region_20km_avg_ndvi', 'region_20km_avg_elevation', 'region_20km_avg_soil_moisture', 'region_20km_max_elev', 
    'region_20km_min_elev', 'region_20km_elev_range')] <- 'Regional (20km) geography'
  p$type[p$explanatory %in% c('abs_latitude', 'latitude', 'longitude')] <- 'Spatial'
  p
}
```

```{r}
explanatory_labels = c(
  'has_introduced_species'='Has introduced species', 
  'city_avg_ndvi'='Average NDVI', 
  'city_avg_elevation'='Average elevation', 
  'city_avg_temp'='Average temperature', 
  'city_avg_min_monthly_temp'='Average minimum monthly temperature', 
  'city_avg_max_monthly_temp'='Average maximum monthly temperature', 
  'city_avg_monthly_temp'='Average monthly temperature', 
  'city_avg_rainfall'='Average rainfall', 
  'city_avg_max_monthly_rainfall'='Average maximum monthly rainfall', 
  'city_avg_min_monthly_rainfall'='Average minimum monthly rainfall', 
  'city_avg_soil_moisture'='Average soil moisture', 
  'city_max_elev'='Maximum elevation', 
  'city_min_elev'='Minimum elevation', 
  'city_elev_range'='Elevation range', 
  'region_20km_avg_ndvi'='Average NDVI', 
  'region_20km_avg_elevation'='Average elevation', 
  'region_20km_avg_soil_moisture'='Average soil moisture', 
  'region_20km_max_elev'='Maximum elevation', 
  'region_20km_min_elev'='Minimum elevation',
  'region_20km_elev_range'='Elevation range',
  'region_50km_avg_ndvi'='Average NDVI',
  'region_50km_avg_elevation'='Average elevation',
  'region_50km_avg_soil_moisture'='Average soil moisture', 
  'region_50km_max_elev'='Maximum elevation',
  'region_50km_min_elev'='Minimum elevation', 
  'region_50km_elev_range'='Elevation range',
  'abs_latitude' = 'Absolute latitude',
  'latitude' = 'Latitude',
  'longitude' = 'Longitude',
  'core_realmAfrotropic' = 'Afrotropical', 
  'core_realmAustralasia' = 'Austaliasian', 
  'core_realmIndomalayan' = 'Indomalayan', 
  'core_realmNearctic' = 'Nearctic', 
  'core_realmNeotropic' = 'Neotropical',
  'core_realmPalearctic' = 'Palearctic',
  'core_realmOceania' = 'Oceanical')
```

## Helper plot functions
```{r}
geom_normalised_histogram = function(name, gg, legend.position = "bottom") {
  gg + 
    geom_histogram(aes(fill = core_realm), binwidth = 0.1, position = "dodge") +
    geom_vline(aes(xintercept = 0.5), color = "#000000", size = 0.4) +
    geom_vline(aes(xintercept = 0), color = "#000000", size = 0.2, linetype = "dashed") +
    geom_vline(aes(xintercept = 1), color = "#000000", size = 0.2, linetype = "dashed") + 
    ylab("Number of cities") + xlab("Normalised Response") + ylim(c(0, 70)) +
    labs(title = name, fill = 'Realm') +
    theme_bw() +
    theme(legend.position=legend.position)
}
```

```{r}
geom_map = function(map_sf, title) {
  norm_mntd_analysis_geo = ggplot() + 
    geom_sf(data = world_map, aes(geometry = geometry)) +
    map_sf +
    normalised_colours_scale +
    labs(colour = 'Normalised\nResponse') +
    theme_bw() +
    theme(legend.position="bottom")
}
```

## Helper Dredge functions
```{r}
# Taken from MuMIN package
# https://rdrr.io/cran/MuMIn/src/R/averaging.R
# https://rdrr.io/cran/MuMIn/src/R/model.avg.R

.coefarr.avg <-
  function(cfarr, weight, revised.var, full, alpha) {	
    weight <- weight / sum(weight)
    nCoef <- dim(cfarr)[3L]
    if(full) {
      nas <- is.na(cfarr[, 1L, ]) & is.na(cfarr[, 2L, ])
      cfarr[, 1L, ][nas] <- cfarr[, 2L, ][nas] <- 0
      #cfarr[, 1L:2L, ][is.na(cfarr[, 1L:2L, ])] <- 0
      if(!all(is.na(cfarr[, 3L, ])))
        cfarr[ ,3L, ][is.na(cfarr[ , 3L, ])] <- Inf
    }
    
    avgcoef <- array(dim = c(nCoef, 5L),
                     dimnames = list(dimnames(cfarr)[[3L]], c("Estimate",
                                                              "Std. Error", "Adjusted SE", "Lower CI", "Upper CI")))
    for(i in seq_len(nCoef))
      avgcoef[i, ] <- par.avg(cfarr[, 1L, i], cfarr[, 2L, i], weight,
                              df = cfarr[, 3L, i], alpha = alpha, revised.var = revised.var)
    
    avgcoef[is.nan(avgcoef)] <- NA
    return(avgcoef)
  }

.makecoefmat <- function(cf) {
  no.ase <- all(is.na(cf[, 3L]))
  z <- abs(cf[, 1L] / cf[, if(no.ase) 2L else 3L])
  pval <- 2 * pnorm(z, lower.tail = FALSE)
  cbind(cf[, if(no.ase) 1L:2L else 1L:3L, drop = FALSE],
        `z value` = z, `Pr(>|z|)` = zapsmall(pval))
}

# Generate model selections using lmer, dredge, and model.avg
# `forumla` : a two-sided linear formula object describing both the fixed-effects and random-effects part of the model
# `data` : the data frame containing the variables from the formula
# `aic_delta` : the AIC delta to use for selecting models in model average
model_average <- function(formula, data, aic_delta = 20) {
  model <- lm(
    formula,
    data=data
  )
  dredge_result <- dredge(model)
  summary(model.avg(dredge_result, subset = delta < aic_delta))
}

# Create a summary data frame containing the selected variables from a model
# `model_sum` : The model summary output from `model_average`
model_summary <- function(model_sum) {
  .column_name <- function(postfix) {
    postfix
  }
  
  # just return the estimate and p value
  weight <- model_sum$msTable[, 5L]
  
  coefmat.full <- as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
                                                          attr(model_sum, "revised.var"), TRUE, 0.05)))
  
  coefmat.subset <-
    as.data.frame(.makecoefmat(.coefarr.avg(model_sum$coefArray, weight,
                                            attr(model_sum, "revised.var"), FALSE, 0.05)))
  
  
  coefmat.subset <- coefmat.subset[-c(1), c(1, 2, 5)]
  names(coefmat.subset) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
  coefmat.subset <- tibble::rownames_to_column(coefmat.subset, "explanatory")
  coefmat.subset$model = 'subset'
  
  coefmat.full <- coefmat.full[-c(1), c(1, 2, 5)]
  names(coefmat.full) <- c(.column_name("estimate"), .column_name("error"), .column_name("p"))
  coefmat.full <- tibble::rownames_to_column(coefmat.full, "explanatory")
  coefmat.full$model = 'full'
  
  rbind(coefmat.full, coefmat.subset)
}
```

```{r}
formula_from_vsurp = function(predictors, dependent, vsurp_result) {
  as.formula(paste(dependent, paste(names(predictors[,vsurp_result$varselect.interp]), collapse="+"), sep = "~"))
}
```

```{r}
plot_vsurp_result = function(result_table) {
  plot = result_table[result_table$model == 'full',]
  plot = type_labels(plot)

  ggplot(plot, aes(y=explanatory, x=estimate, colour = type)) + 
    geom_line() +
    geom_point()+
    geom_errorbar(aes(xmin=estimate-error, xmax=estimate+error), width=.2,
                   position=position_dodge(0.05)) +
    scale_y_discrete(
      limits = rev(levels(plot$explanatory)), 
      labels = explanatory_labels) +
    theme_bw() +
    geom_vline(xintercept=0, linetype="dotted") +
    guides(colour=guide_legend(title="Predictor type")) + xlab('Increase from 0 (habitat filtering)\nto 1 (competitive exclusion)\n± Standard Error') + ylab('Predictor') +
    theme(legend.justification = "top")
}
```

## MNTD
```{r}
norm_mntd_analysis_plot = geom_normalised_histogram(
  'MNTD', 
  ggplot(analysis_data, aes(mntd_normalised))
)
norm_mntd_analysis_plot
```

```{r}
norm_mntd_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = mntd_normalised, geometry = geometry)), 'MNTD')
norm_mntd_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_mntd_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
```

```{r}
norm_mntd_analysis_data = model_data(analysis_data[!is.na(analysis_data$mntd_normalised),], 'mntd_normalised')
norm_mntd_analysis_predictors = norm_mntd_analysis_data[,-1]
norm_mntd_analysis_interp = VSURF(x = norm_mntd_analysis_predictors, y = norm_mntd_analysis_data$mntd_normalised)
names(norm_mntd_analysis_predictors[,norm_mntd_analysis_interp$varselect.interp])
```

```{r}
norm_mntd_analysis_formula = formula_from_vsurp(norm_mntd_analysis_predictors, "mntd_normalised", norm_mntd_analysis_interp)
norm_mntd_analysis_result <- model_average(norm_mntd_analysis_formula, norm_mntd_analysis_data)
norm_mntd_analysis_result_table = model_summary(norm_mntd_analysis_result)
norm_mntd_analysis_result_table
```

```{r}
norm_mntd_analysis_pred_plot = plot_vsurp_result(norm_mntd_analysis_result_table)
norm_mntd_analysis_pred_plot
```

## Gape width - FDiv
```{r}
norm_gape_fdiv_analysis_plot = geom_normalised_histogram(
  'Gape FDiv', 
  ggplot(analysis_data, aes(gape_width_fdiv_normalised))
)
norm_gape_fdiv_analysis_plot
```

```{r}
norm_gape_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = gape_width_fdiv_normalised, geometry = geometry)), 'Gape Width FDiv')
norm_gape_fdiv_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_gape_width_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
```

```{r}
norm_gape_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$gape_width_fdiv_normalised),], 'gape_width_fdiv_normalised')
norm_gape_fdiv_analysis_predictors = norm_gape_fdiv_analysis_data[,-1]
norm_gape_fdiv_analysis_interp = VSURF(x = norm_gape_fdiv_analysis_predictors, y = norm_gape_fdiv_analysis_data$gape_width_fdiv_normalised)
names(norm_gape_fdiv_analysis_predictors[,norm_gape_fdiv_analysis_interp$varselect.interp])
```

```{r}
norm_gape_fdiv_analysis_formula = formula_from_vsurp(norm_gape_fdiv_analysis_predictors, "gape_width_fdiv_normalised", norm_gape_fdiv_analysis_interp)
norm_gape_fdiv_analysis_result <- model_average(norm_gape_fdiv_analysis_formula, norm_gape_fdiv_analysis_data)
norm_gape_fdiv_analysis_result_table = model_summary(norm_gape_fdiv_analysis_result)
norm_gape_fdiv_analysis_result_table
```

```{r}
norm_gape_fdiv_analysis_pred_plot = plot_vsurp_result(norm_gape_fdiv_analysis_result_table)
norm_gape_fdiv_analysis_pred_plot
```


## HWI - FDiv
```{r}
norm_hwi_fdiv_loco_analysis_plot = geom_normalised_histogram(
  'HWI FDiv', 
  ggplot(analysis_data, aes(handwing_index_fdiv_normalised))
)
norm_hwi_fdiv_loco_analysis_plot
```

```{r}
norm_hwi_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = handwing_index_fdiv_normalised, geometry = geometry)), 'HWI FDiv')
norm_hwi_fdiv_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_hwi_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
```

```{r}
norm_hwi_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$handwing_index_fdiv_normalised),], 'handwing_index_fdiv_normalised')
norm_hwi_fdiv_analysis_predictors = norm_hwi_fdiv_analysis_data[,-1]
norm_hwi_fdiv_analysis_interp = VSURF(x = norm_hwi_fdiv_analysis_predictors, y = norm_hwi_fdiv_analysis_data$handwing_index_fdiv_normalised)
names(norm_hwi_fdiv_analysis_predictors[,norm_hwi_fdiv_analysis_interp$varselect.interp])
```


```{r}
norm_hwi_fdiv_analysis_formula = formula_from_vsurp(norm_hwi_fdiv_analysis_predictors, "handwing_index_fdiv_normalised", norm_hwi_fdiv_analysis_interp)
norm_hwi_fdiv_analysis_result <- model_average(norm_hwi_fdiv_analysis_formula, norm_hwi_fdiv_analysis_data)
norm_hwi_fdiv_analysis_result_table = model_summary(norm_hwi_fdiv_analysis_result)
norm_hwi_fdiv_analysis_result_table
```

```{r}
norm_hwi_fdiv_analysis_pred_plot = plot_vsurp_result(norm_hwi_fdiv_analysis_result_table)
norm_hwi_fdiv_analysis_pred_plot
```


## Mass - FDiv
```{r}
norm_mass_fdiv_loco_analysis_plot = geom_normalised_histogram(
  'Mass FDiv', 
  ggplot(analysis_data, aes(mass_fdiv_normalised))
)
norm_mass_fdiv_loco_analysis_plot
```

```{r}
norm_mass_fdiv_analysis_geo_plot = geom_map(geom_sf(data = analysis_data, aes(color = mass_fdiv_normalised, geometry = geometry)), 'Mass FDiv')
norm_mass_fdiv_analysis_geo_plot
ggsave(filename(FIGURES_OUTPUT_DIR, 'normalised_mass_using_abundance.jpg'), width = 2500, height=1100, units = 'px')
```

```{r}
norm_mass_fdiv_analysis_data = model_data(analysis_data[!is.na(analysis_data$mass_fdiv_normalised),], 'mass_fdiv_normalised')
norm_mass_fdiv_analysis_predictors = norm_mass_fdiv_analysis_data[,-1]
norm_mass_fdiv_analysis_interp = VSURF(x = norm_mass_fdiv_analysis_predictors, y = norm_mass_fdiv_analysis_data$mass_fdiv_normalised)
names(norm_mass_fdiv_analysis_predictors[,norm_mass_fdiv_analysis_interp$varselect.interp])
```

```{r}
norm_mass_fdiv_analysis_formula = formula_from_vsurp(norm_mass_fdiv_analysis_predictors, "mass_fdiv_normalised", norm_mass_fdiv_analysis_interp)
norm_mass_fdiv_analysis_result <- model_average(norm_mass_fdiv_analysis_formula, norm_mass_fdiv_analysis_data)
norm_mass_fdiv_analysis_result_table = model_summary(norm_mass_fdiv_analysis_result)
norm_mass_fdiv_analysis_result_table
```

```{r}
norm_mass_fdiv_analysis_pred_plot = plot_vsurp_result(norm_mass_fdiv_analysis_result_table)
norm_mass_fdiv_analysis_pred_plot
```
# Create plot of differences in process response
```{r}
pred_legend <- get_legend(
  # create some space to the left of the legend
  norm_hwi_fdiv_analysis_pred_plot + theme(legend.box.margin = margin(0, 0, 0, 12))
)
geo_legend <- get_legend(
  # create some space to the left of the legend
  norm_mass_fdiv_analysis_geo_plot + theme(legend.box.margin = margin(0, 0, 0, 12))
)

plot_grid(
  plot_grid(
    norm_mntd_analysis_geo_plot + theme(legend.position="none"), 
    norm_mntd_analysis_pred_plot + theme(legend.position="none"), 
    nrow = 1
  ) + draw_label("MNTD", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    norm_gape_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    norm_gape_fdiv_analysis_pred_plot + theme(legend.position="none"), 
    nrow = 1
  ) + draw_label("Gape", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    norm_hwi_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    norm_hwi_fdiv_analysis_pred_plot + theme(legend.position="none"), 
    nrow = 1
  ) + draw_label("HWI", size = 16, angle = 90, x = 0.01, y = 0.5),
  plot_grid(
    norm_mass_fdiv_analysis_geo_plot + theme(legend.position="none"), 
    norm_mass_fdiv_analysis_pred_plot + theme(legend.position="none"), 
    nrow = 1
  ) + draw_label("Mass", size = 16, angle = 90, x = 0.01, y = 0.5), 
  plot_grid(
    geo_legend, 
    pred_legend, 
    nrow = 1
  ),
  nrow = 5
)
ggsave(filename(FIGURES_OUTPUT_DIR, 'process_response.jpg'), width = 2500, height=5000, units = 'px')
```


# Compare metrics against each other
```{r}
ggplot(analysis_data, aes(x = gape_width_fdiv_normalised, y = mntd_normalised, colour = core_realm)) + 
  geom_point() +
  ylab("MNTD") + 
  xlab("Gape Width FDiv") +
  theme_bw() + labs(color = "Realm")
```

```{r}
ggplot(analysis_data, aes(x = handwing_index_fdiv_normalised, y = mntd_normalised, colour = core_realm)) + 
  geom_point() +
  ylab("MNTD") + 
  xlab("HWI FDiv") +
  theme_bw() + labs(color = "Realm")
```

```{r}
ggplot(analysis_data, aes(x = handwing_index_fdiv_normalised, y = gape_width_fdiv_normalised, colour = core_realm)) + 
  geom_point() +
  ylab("Gape Width FDiv") + 
  xlab("HWI FDiv") +
  theme_bw() + labs(color = "Realm")
```

```{r}
mntd_fdiv_analysis = analysis_data %>% 
  dplyr::select(city_id,  mntd_normalised, handwing_index_fdiv_normalised, gape_width_fdiv_normalised) %>%
  left_join(community_summary) %>%
  mutate(urban_pool_perc = urban_pool_size * 100 / regional_pool_size)
mntd_fdiv_analysis
```

```{r}
ggpairs(mntd_fdiv_analysis %>% dplyr::select(mntd_normalised, handwing_index_fdiv_normalised, gape_width_fdiv_normalised, regional_pool_size, urban_pool_size, urban_pool_perc))
ggsave(filename(FIGURES_OUTPUT_DIR, 'appendix_normalised_correlation.jpg'))
```


